Category: 7. Maths

In this section, we describe the method used to generate the QUAPOS dataset, including the selection of the different scenarios (transmitter’s, receivers’, and channel effects’ configuration), the processing algorithms (image points…

This section presents the FARNet model (weighted Fusion of channel and spatial Attention in Residual connections Network), designed for DFER. This model is lightweight and suitable for deployment on a low-power edge device, enabling real-time…

In this section, we examine the theoretical aspects of the model under consideration.

Existence of the solution to the considered fractional PC model via fixed-point theory

In this part, we analyze the guarantees provided by the Leray-Schauder and…

This research develops a new HBECSA-DLMND methodology for Secure Distributed Systems. The approach follows the concept of metaheuristic feature selection with DL-based detection of malicious nodes in distributed systems. Figure 1 depicts the…

Different shapes and sizes of trajectories to be tracked are used to train and validate designed structures of neural networks , such as regression multilayer perceptron network (MLP) with a single hidden layer having N neurons (\(MLP\_1\_N\) ),…

Researchers have categorized path-planning techniques based on adopted methodologies and other criteria. The various classifications of Path-planning techniques are Global or Local path planning based on the data received from the environment,…

This paper proposes a method for automatically measuring proximal femoral morphological parameters, as shown in Fig. 1. The method first constructs a statistical model of the femur with generalized features using manually segmented point cloud…

Experimental parameterization

In the context of Agricultural Wireless Sensor Networks (AWSNs), the arrangement of sensor nodes is pivotal to network performance, with a primary concern being the dual objective of maximizing target area coverage…

Based on the aforementioned definition, this study proposes STFGCN. The model structure is illustrated in Fig. 2. It consists of three core modules: matrix fusion module, spatial feature extraction module, and temporal feature extraction module,…

Suppose \(X = [e_{ij}]_{p \times n}\) is a data matrix of gene expression values with p features and n observations, where \(i = 1, 2, 3, \ldots , p\), \(j = 1, 2, 3, \ldots , n\), and \(Y \in \{0, 1\}\) is the corresponding binary response. The…